Python 3.12+ drops optional GIL-free mode

Learning Python: Today’s Lesson on User Input Validation Today, while working on a Python project involving conditional statements, logical operators, nested if blocks, and the modulo operator, I encountered a ValueError. The issue was simple but important: Python’s int() function cannot process numbers entered with commas (e.g., 250,000). Instead of treating it as a setback, I learned a key real-world lesson: User input is unpredictable, and good programs must account for that. I solved the problem by sanitizing the input before conversion, allowing the program to handle values like 250,000 or 1,000,000 safely. This reinforced an important principle in software development: Writing code is not just about making it work, but making it resilient. Still pushing forward with my learning and building better habits one project at a time. #Python #LearningInPublic #100DaysOfCode #SoftwareDevelopment #TechJourney #ProgrammingBasics #ProblemSolving

Have you ever felt like you know Python… but everything is disorganized in your head? You learn variables. Then functions. Then classes. And suddenly Python works… but it doesn’t really make sense yet. That’s the problem... learning isolated concepts is not the same as understanding the language. When you don’t see the bigger picture, programming feels like connecting pieces without knowing what image you’re building. I’m sharing this map as a summary where you can see: • Which parts form the core of the language • How logic flows when the program makes decisions • How data is organized • How functions and classes connect within a project • Why Python scales so well for real-world projects Having this kind of clarity changes how you study, how you practice, and how you approach real problems with code. Do you already work with Python? I’d love to hear about your experience. #Python #LearnPython #Programming #SoftwareDevelopment #ConceptMap

🔍 Why Python doesn’t have i++ like C/C++ 🐍 Today I learned something interesting while learning Python from a YouTube video by Gowtham Data Engineering: https://lnkd.in/gvxmy27F In C / C++, we commonly use: i++; But in Python, i++ does not exist ❌ Instead, Python uses: i += 1 💡 Why Python avoids i++? Python focuses on readability & simplicity i++ can cause confusion (pre-increment vs post-increment) i += 1 is clear, explicit, and Pythonic Python avoids unnecessary operators to keep code clean This helped me understand Python’s design philosophy much better 👍 Small concepts like this really matter when learning programming deeply. Thanks Gowtham SB for the clear explanation 🙌 Learning step by step 🚀 #Python #LearningPython #Programming #Cplusplus #DataEngineering #Students #CodingJourney

Here’s a simple yet powerful Python program that demonstrates how functions and loops work together to solve real-world problems efficiently. 🔹 In this program, we define a function even(li) that: Takes a list as input Iterates through each element Checks whether the number is even Prints only the even numbers from the list This is a great example for beginners to understand: ✔ Functions in Python ✔ Loops (for) ✔ Conditional statements (if) ✔ Modulus operator (%) Such small programs build strong logical thinking and problem-solving skills in programming. def even(li):   for i in li:     if i%2 == 0 :       print(i,end=" ") lst= [1,2,3,4,5,6,7,8,9,10] even(lst) #Python #Coding #ProgrammingBasics #DataScience #Learning #TechEducation

Today’s Learning: File Handling in Python Today, I explored File Handling in Python, focusing on how programs interact with external files to store, read, and manage data efficiently. Key concepts covered: Understanding file handling and its real-world use cases File modes: r, w, a, x, r+, w+, a+ Reading data using read(), readline(), and readlines() Writing and appending content to files Best practices like closing files and using the with statement Hands-on practice helped me understand how persistent data storage works beyond variables and memory, which is a crucial concept for backend development and automation. GitHub repository for today’s practice: https://lnkd.in/gsdbxrzZ Consistent learning and daily practice continue to strengthen my Python fundamentals. #Python #PythonLearning #FileHandling #BackendDevelopment #ProgrammingBasics #CodingPractice #SoftwareDevelopment #DeveloperJourney #LearningEveryday #Consistency #CareerGrowth #TechSkills #GitHubProjects #ProblemSolving

🚀 Python Practice | Finding the Second Largest Number in a List 🐍 Today I practiced a basic Python logic problem: finding the second largest number in a list. At first glance, it looks easy — but problems like these are crucial because they strengthen: -Logical thinking -Loop and condition handling -Understanding how code works behind the scenes (without shortcuts) 📌 Approach: -First loop to find the largest number -Second loop to find the second largest number less than the largest -Used basic loops and conditional logic for better clarity I solved this without using built-in functions to focus purely on fundamentals. ✨ This helped me strengthen my understanding of: -Loops & conditionals -Logical thinking -Writing optimized beginner-friendly code #Python #CodingPractice #ProgrammingBasics #LearningJourney #ProblemSolving #DSA #KeepLearning

Day 2 of Python. Building logic before libraries. Today I deliberately avoided Pandas and NumPy. Instead, I focused on the part that controls everything later: core Python logic. What I worked on: Variables and data types Lists, tuples, dictionaries, sets Conditional logic Loops and flow control The key realization: Libraries don’t make code powerful. Logic does. If logic is weak: Scripts break silently Pipelines fail unexpectedly Debugging becomes guesswork Strong Python fundamentals make everything easier later: Cleaner Pandas transformations Predictable data validation Reliable automation This phase is about training how I think, not what I import. Learning step by step and building confidence in how Python executes, not just how it looks. Tomorrow: functions, modular thinking, and reusable code blocks. If you work with Python: Which basic concept helped you the most early on? #datawithanurag #dataxbootcamp #python #pandas #numpy #datatypes

Python development in 2026 feels different in a practical way. Type hints are no longer “nice to have” — they shape how teams design APIs, validate data, and refactor safely. Async code is more common in everyday projects, not just high-scale systems. The biggest shift for me: Python code today is written to be read, reviewed, and maintained by teams — not just executed by machines. #Python #BackendDevelopment #SoftwareEngineering

🐍 Ever wondered what REALLY happens when you run a Python program? You type: 👉 python app.py But behind the scenes… a LOT is happening 👀 🔹 1. Python Interpreter Starts Python launches the interpreter (CPython for most of us). It reads your file line by line. 🔹 2. Code → Bytecode Your Python code is converted into **bytecode** (.pyc files). 👉 This makes execution faster next time. 🔹 3. Python Virtual Machine (PVM) The bytecode runs inside the PVM. This is where loops, conditions, functions actually execute. 🔹 4. Memory Management Python automatically: ✔ allocates memory ✔ tracks object references ✔ clears unused objects (Garbage Collection ♻️) 🔹 5. C Under the Hood Most Python operations are powered by **C code** That’s why Python feels simple but still powerful 💪 ✨ That’s the magic: Simple syntax on the surface, serious engineering underneath. 💡 Knowing this helps you: • Write faster code • Debug better • Understand performance issues #Python #BackendDevelopment #SoftwareEngineering #Programming #LearnPython #TechSimplified

🐍 90 Days of Python – Day 32 Polymorphism in Python | One Interface, Multiple Forms Today, I explored Polymorphism, one of the core pillars of Object-Oriented Programming in Python. Polymorphism allows the same method or function name to behave differently based on the object or context. 🔹 Concepts covered today: ✅ Method Overriding ✅ Same method name, different behavior ✅ Polymorphism using inheritance ✅ Runtime polymorphism ✅ Using polymorphism with functions ✅ Duck Typing concept in Python Polymorphism helps in: Writing flexible and scalable code Reducing code duplication Improving maintainability Designing clean APIs and systems This concept is widely used in: Frameworks & libraries Large-scale applications Machine learning pipelines Real-world software design 📌 Day 32 completed — mastering flexible behavior in Python programs. 👉 Next up: Abstraction & advanced OOP concepts 🚀 #90DaysOfPython #Day32 #PythonOOP #Polymorphism #LearningInPublic #PythonDeveloper #CleanCode #SoftwareEngineering